期刊
JOURNAL OF PROTEOME RESEARCH
卷 2, 期 4, 页码 405-412出版社
AMER CHEMICAL SOC
DOI: 10.1021/pr0340227
关键词
bioinformatics; micro-array; text mining; gene-disease association; breast cancer
资金
- NHLBI NIH HHS [HL66582-02] Funding Source: Medline
High-throughput technologies, such as proteomic screening and DNA micro-arrays, produce vast amounts of data requiring comprehensive analytical methods to decipher the biologically relevant results. One approach would be to manually search the biomedical literature; however, this would be an arduous task. We developed an automated literature-mining tool, termed MedGene, which comprehensively summarizes and estimates the relative strengths of all human gene-disease relationships in Medline. Using MedGene, we analyzed a novel micro-array expression dataset comparing breast cancer and normal breast tissue in the context of existing knowledge. We found no correlation between the strength of the literature association and the magnitude of the difference in expression level when considering changes as high as 5-fold; however, a significant correlation was observed (r = 0.41; p = 0.05) among genes showing an expression difference of 10-fold or more. Interestingly, this only held true for estrogen receptor (ER) positive tumors, not ER negative. MedGene identified a set of relatively understudied, yet highly expressed genes in ER negative tumors worthy of further examination.
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